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Related Experiment Video

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Pavlovian Conditioned Approach Training in Rats

Pavlovian Conditioned Approach Training in Rats

Published on: February 4, 2016

An information-theoretic approach to curiosity-driven reinforcement learning.

Susanne Still¹, Doina Precup

¹Information and Computer Sciences, University of Hawaii at Mānoa, Honolulu, HI 96822, USA. sstill@hawaii.edu

Theory in Biosciences = Theorie in Den Biowissenschaften

|July 14, 2012

Summary

This study reframes exploration in reinforcement learning using information theory. It reveals Boltzmann exploration is optimal and proposes curiosity-driven learning maximizes predictive power for better exploration-exploitation trade-offs.

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Area of Science:

Artificial Intelligence
Information Theory
Machine Learning

Background:

Reinforcement learning (RL) relies on exploration strategies to balance reward acquisition and learning.
Boltzmann-style exploration is a common method, but its theoretical optimality is re-examined.
Curiosity-driven learning aims to enhance agent engagement and knowledge acquisition.

Purpose of the Study:

To provide an information-theoretic perspective on exploration in reinforcement learning.
To demonstrate the optimality of Boltzmann-style exploration concerning expected return and policy coding cost.
To introduce a novel framework for curiosity-driven learning that integrates predictive power maximization.

Main Methods:

Information-theoretic analysis of exploration strategies.
Derivation of optimal policies balancing expected return and information gain.
Formulation of a new exploration-exploitation trade-off based on predictive power.

Main Results:

Boltzmann-style exploration is shown to be information-theoretically optimal.
A novel exploration bonus is introduced, enhancing curiosity-driven learning.
The proposed exploration-exploitation trade-off is inherent in optimal deterministic policies.

Conclusions:

Exploration in reinforcement learning can be viewed as optimizing information gain.
Curiosity-driven learning enhances agent performance by maximizing predictive power.
This approach offers a more principled understanding of exploration beyond random action selection.